Giorgio Leonardi

Learn More
OBJECTIVE In the present paper, we describe an application of case-based retrieval to the domain of end stage renal failure patients, treated with hemodialysis. MATERIALS AND METHODS Defining a dialysis session as a case, retrieval of past similar cases has to operate both on static and on dynamic features, since most of the monitoring variables of a(More)
In a competitive health-care market, hospitals have to focus on ways to streamline their processes in order to deliver high quality care while at the same time reducing costs. To accomplish this goal, hospital managers need a thorough understanding of the actual processes. Diffusion of Information and Communication Technology tools within hospitals, such as(More)
The management of chronic and out-patients is a complex process which requires the cooperation of different agents belonging to several organizational units. Patients have to move to different locations to access the necessary services and to communicate their health status data. From their point of view there should be only one organization (Virtual(More)
Time series retrieval is a critical issue in all domains in which the observed phenomenon dynamics have to be dealt with. In this paper, we propose a novel, domain independent time series retrieval framework, based on Temporal Abstractions (TA). Our framework allows for multilevel abstractions, according to two dimensions, namely a taxonomy of (trend or(More)
In this work we propose a case-based architecture tackling the problem of configuring and processing temporal abstractions (trends and qualitative states) produced from raw time series data. The parameter configuration is a critical problem in many temporal abstraction processes; in several application domains (especially in medical ones), contextual(More)
Interpreting time series of measurements and exploring a repository of cases with time series data looking for similarities, are nontrivial, but very important tasks. Classical methodological solutions proposed to deal with (some of) these goals, typically based on mathematical techniques, are characterized by strong limitations, such as unclear or(More)
We developed an ontology that allows representation and reasoning with effects of clinical actions. The ontology can support three important use-cases: (1) summarization and explanation of observed clinical states, (2) enhancing patient safety using safety rules, and (3) assessing guideline compliance. In this paper we focus on explanation of observed(More)